A selection of initiatives with real problems, real decisions, and real outcomes.
Off-the-shelf health scoring tools don't account for the specifics of our customer base. We needed something built for us.
Health signals mean different things depending on customer type and contract size. A single score applied across the board would misread risk in every direction.
An in-house score combining product usage, engagement, and sentiment data. The model adjusts its weights by customer segment and contract size, so every tier gets an accurate read.
CS teams can prioritize their books and know which action to take, not just which accounts are at risk. Less time in spreadsheets, more time with customers. Top-tier accounts hold 92%+ retention consistently.
The company needed a fiscal year plan leadership could actually operate against, not just a top-down revenue target with nothing underneath it.
Annual planning lacked granularity. Leadership had a revenue target but no clear line of sight into what inputs,leads, conversion rates, headcount, territory mix,would get us there.
A bottoms-up model projecting Bookings, Churn, and Net Revenue from first principles. The model captures leads by source, lead-to-MQL rate, MQL-to-SQL rate by source, Opp-to-Close rate by source and team, ASP by region and team, and retention rates across 6 distinct customer cohorts.
Became the cornerstone for in-year tracking, giving leadership a living benchmark to evaluate performance against plan at every stage of the funnel.
We decided a Pod structure would serve customers better. Executing it without disrupting 150+ active relationships was the hard part.
Structural changes in CS almost always create customer risk. We needed to redesign territory alignments, reassign accounts across CSM and AM Pods, and establish new cohorts,all while maintaining continuity and trust.
New ACV-based cohorts and territory alignments across the full customer base. Strategic account-to-CSM/AM Pod matching to preserve relationship continuity. A transition playbook that led every conversation with customer value, not internal operational language.
150+ customer transitions completed in 3 months. One negative piece of feedback. Zero transition-related churn.
A set of analytics and reporting products that give leadership the visibility to make decisions before problems compound,from deal-level pipeline to company-level trajectory.
A standing cadence examining pipeline strength and weakness by source, assigning specific next steps and owners. Turns data into action within a week, not a quarter.
Weekly new business, upsell, and retention forecast with an analytics lens on current-quarter positioning and coverage for upcoming quarters against targets.
Deep-dive analytics sessions benchmarking actual vs. model,driving strategic conversations that determine company direction.
Platform adoption tracking by feature,identifying growth signals and decline risks, and connecting those trends to retention and expansion outcomes.
Full-cycle ownership of comp plan design, quota setting, payout execution, and the software systems that run the program across all GTM teams.
Ongoing config and optimization of our CS management tooling,ensuring CSMs and AMs always have accurate data, health scores, and automated plays.
Big initiatives are a fraction of the job. These are the recurring programs that run every week and keep the revenue org from flying blind.
Weekly bookings, upsell, and retention forecast with a quantitative lens on quarter coverage,presented to senior leadership every week without fail.
Structured weekly examination of pipeline health by source, identifying risks, and driving specific next steps with clear ownership and timelines.
Full-cycle ownership of comp plan design, quota setting, payout execution, and the systems that run the program across all GTM teams.
Quarterly analytics deep dives benchmarking actual performance against our operating model and driving strategic company-level conversations.
Ongoing configuration and optimization of our CS management tooling,ensuring CSMs and AMs have accurate data, health scores, and automated plays.
Regular readouts on platform feature adoption trends,identifying growth signals and decline risks, and connecting those patterns to retention outcomes.
Building a RevOps function, thinking through GTM infrastructure, or just want to compare notes,reach out.